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Privacy risks of whole-slide image sharing in digital pathology
Access to large volumes of so-called whole-slide images—high-resolution scans of complete pathological slides—has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a method...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160114/ https://www.ncbi.nlm.nih.gov/pubmed/37142591 http://dx.doi.org/10.1038/s41467-023-37991-y |
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author | Holub, Petr Müller, Heimo Bíl, Tomáš Pireddu, Luca Plass, Markus Prasser, Fabian Schlünder, Irene Zatloukal, Kurt Nenutil, Rudolf Brázdil, Tomáš |
author_facet | Holub, Petr Müller, Heimo Bíl, Tomáš Pireddu, Luca Plass, Markus Prasser, Fabian Schlünder, Irene Zatloukal, Kurt Nenutil, Rudolf Brázdil, Tomáš |
author_sort | Holub, Petr |
collection | PubMed |
description | Access to large volumes of so-called whole-slide images—high-resolution scans of complete pathological slides—has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a methodology based on risk analysis for evaluating the privacy risks associated with sharing such imaging data and applying the principle “as open as possible and as closed as necessary” is still lacking. In this article, we develop a model for privacy risk analysis for whole-slide images which focuses primarily on identity disclosure attacks, as these are the most important from a regulatory perspective. We introduce a taxonomy of whole-slide images with respect to privacy risks and mathematical model for risk assessment and design . Based on this risk assessment model and the taxonomy, we conduct a series of experiments to demonstrate the risks using real-world imaging data. Finally, we develop guidelines for risk assessment and recommendations for low-risk sharing of whole-slide image data. |
format | Online Article Text |
id | pubmed-10160114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101601142023-05-06 Privacy risks of whole-slide image sharing in digital pathology Holub, Petr Müller, Heimo Bíl, Tomáš Pireddu, Luca Plass, Markus Prasser, Fabian Schlünder, Irene Zatloukal, Kurt Nenutil, Rudolf Brázdil, Tomáš Nat Commun Article Access to large volumes of so-called whole-slide images—high-resolution scans of complete pathological slides—has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a methodology based on risk analysis for evaluating the privacy risks associated with sharing such imaging data and applying the principle “as open as possible and as closed as necessary” is still lacking. In this article, we develop a model for privacy risk analysis for whole-slide images which focuses primarily on identity disclosure attacks, as these are the most important from a regulatory perspective. We introduce a taxonomy of whole-slide images with respect to privacy risks and mathematical model for risk assessment and design . Based on this risk assessment model and the taxonomy, we conduct a series of experiments to demonstrate the risks using real-world imaging data. Finally, we develop guidelines for risk assessment and recommendations for low-risk sharing of whole-slide image data. Nature Publishing Group UK 2023-05-04 /pmc/articles/PMC10160114/ /pubmed/37142591 http://dx.doi.org/10.1038/s41467-023-37991-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Holub, Petr Müller, Heimo Bíl, Tomáš Pireddu, Luca Plass, Markus Prasser, Fabian Schlünder, Irene Zatloukal, Kurt Nenutil, Rudolf Brázdil, Tomáš Privacy risks of whole-slide image sharing in digital pathology |
title | Privacy risks of whole-slide image sharing in digital pathology |
title_full | Privacy risks of whole-slide image sharing in digital pathology |
title_fullStr | Privacy risks of whole-slide image sharing in digital pathology |
title_full_unstemmed | Privacy risks of whole-slide image sharing in digital pathology |
title_short | Privacy risks of whole-slide image sharing in digital pathology |
title_sort | privacy risks of whole-slide image sharing in digital pathology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160114/ https://www.ncbi.nlm.nih.gov/pubmed/37142591 http://dx.doi.org/10.1038/s41467-023-37991-y |
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